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TuckER: Tensor Factorization for Knowledge Graph Completion

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arxiv 1901.09590 v2 pith:H6HIPFYG submitted 2019-01-28 cs.LG stat.ML

Ivana Balažević , Carl Allen , Timothy M. Hospedales This is my paper
classification cs.LGstat.ML
keywords tuckerfactsknowledgemodelsgraphlinearlinkmodel
verification ladder T0 review T1 audit T2 compute T3 formal
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Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state-of-the-art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A survey of n-ary knowledge representation learning methods proposes a two-dimensional taxonomy based on modeling technique and entity role/position awareness.

  2. AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities

    cs.AI 2025-08 conditional novelty 5.0 of 10

    A zero-training LLM agent that iteratively retrieves and reflects over search results can complete knowledge graph triples about emerging entities better than trained KGC models, the authors report.

  3. Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization

    cs.LG 2025-08 conditional novelty 4.0 of 10

    Generating CPD tensor factors instead of full tensors inside GANs and diffusion models can cut output parameters by roughly 80-90% while keeping similar FID scores on calorimeter data.

  4. Complementarity-driven Representation Learning for Multi-modal Knowledge Graph Completion

    cs.AI 2025-07 reject novelty 4.0 of 10

    MoCME combines expert-network fusion weighted by estimated mutual information and entropy-based negative sampling, and reports state-of-the-art multi-modal knowledge graph completion on five benchmarks.

  5. KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A bidirectional decoder with a graph-aware attention mask, knowledge-masked prediction, and contrastive sub-graph alignment achieves reported state-of-the-art link prediction on Wikidata5M and competitive results on WN18RR.

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